Objective: This is an introductory R walk through, creating a mock data set and then going through various ways in which one can clean it, analyze the data, create some visualizations, and then create maps visualizations from lat and lon coordinates

We start by creating DF_Cats, a mock of what a raw data set might look like. Below we’ll go through some useful general steps in cleaning, analyzing and visualizing results using R.

DF_Cats <- data.frame(
  A = c("Luna","Willow","Name","Miso","Coco","Milo","Bubbles","Socks","Oreo","Fudge", "Muffin",NA, "Muffin"),
  B = c("orange","black","Color","gray","white","gray","brown","brown","black","black","white",NA,"white"),
  C = c(10,9,"Avg. Weight (whole #s)",10,12,10.76,NA,8.1,11,11.876,NA,NA,NA),
  D = c("Denver","Fort Collins","Location","Boulder","Colorado Springs","Denver","Denver","Boulder","Denver","Boulder","Colorado Springs",NA,"Colorado Springs"),
  E = c(4.8,4.5,"Age (round quarter %)",6.3,NA,3.75,10.25,8.5,6,NA,7.25,NA,7.25),
  G = c(NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA),
  H = c(TRUE,TRUE,"Indoor",TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,NA,TRUE),
  I = c("01/14/2025","1/13/2025","Date Surveyed",45636,"12/11/2024","Jan/10/2025","1/6/2025","1/05/2025","08/01/2025","1/8/2025","12/5/2024",NA,"12/5/2024"),
  J = c(39.75115289884762,40.54987369801155,"Lat",40.01748117832861,38.80311438445151,39.74201207575289,39.716228422690236,40.00131035581603,39.74831937335296,40.02695393323672,38.80967904193633,NA,38.80967904193633),
  K = c(-104.99706281032537,-105.0885319787086,"Lon",-105.27776267753427,-104.76783518791748,-104.95473978061132,-104.98374769984966,-105.27534451548036,-104.97590129536343,-105.27441445315193,-104.77443409220167,NA,-104.77443409220167)
)
A B C D E G H I J K
Luna orange 10 Denver 4.8 NA TRUE 01/14/2025 39.7511528988476 -104.997062810325
Willow black 9 Fort Collins 4.5 NA TRUE 1/13/2025 40.5498736980116 -105.088531978709
Name Color Avg. Weight (whole #s) Location Age (round quarter %) NA Indoor Date Surveyed Lat Lon
Miso gray 10 Boulder 6.3 NA TRUE 45636 40.0174811783286 -105.277762677534
Coco white 12 Colorado Springs NA NA TRUE 12/11/2024 38.8031143844515 -104.767835187917
Milo gray 10.76 Denver 3.75 NA TRUE Jan/10/2025 39.7420120757529 -104.954739780611
Bubbles brown NA Denver 10.25 NA TRUE 1/6/2025 39.7162284226902 -104.98374769985
Socks brown 8.1 Boulder 8.5 NA TRUE 1/05/2025 40.001310355816 -105.27534451548
Oreo black 11 Denver 6 NA TRUE 08/01/2025 39.748319373353 -104.975901295363
Fudge black 11.876 Boulder NA NA TRUE 1/8/2025 40.0269539332367 -105.274414453152
Muffin white NA Colorado Springs 7.25 NA TRUE 12/5/2024 38.8096790419363 -104.774434092202
NA NA NA NA NA NA NA NA NA NA
Muffin white NA Colorado Springs 7.25 NA TRUE 12/5/2024 38.8096790419363 -104.774434092202

Cleaning a data frame using the janitor package

Assign column names if stored in a row

Notice that DF_Cats has the column names stored in the third row. row_to_names() can make the specified row become the names of the columns.

#library(dplyr) - Needed for piping ( %>% ) operations
#library(janitor)

DF_Cats = DF_Cats %>% row_to_names(row_number=3, remove_row = TRUE, remove_rows_above = FALSE)

#Here row_to_names() used the specified row number 3 to assign names to the columns. The third row was afterwards removed (default) and the rows above (rows one and two) were not removed.
Name Color Avg. Weight (whole #s) Location Age (round quarter %) NA Indoor Date Surveyed Lat Lon
1 Luna orange 10 Denver 4.8 NA TRUE 01/14/2025 39.7511528988476 -104.997062810325
2 Willow black 9 Fort Collins 4.5 NA TRUE 1/13/2025 40.5498736980116 -105.088531978709
4 Miso gray 10 Boulder 6.3 NA TRUE 45636 40.0174811783286 -105.277762677534
5 Coco white 12 Colorado Springs NA NA TRUE 12/11/2024 38.8031143844515 -104.767835187917
6 Milo gray 10.76 Denver 3.75 NA TRUE Jan/10/2025 39.7420120757529 -104.954739780611
7 Bubbles brown NA Denver 10.25 NA TRUE 1/6/2025 39.7162284226902 -104.98374769985
8 Socks brown 8.1 Boulder 8.5 NA TRUE 1/05/2025 40.001310355816 -105.27534451548
9 Oreo black 11 Denver 6 NA TRUE 08/01/2025 39.748319373353 -104.975901295363
10 Fudge black 11.876 Boulder NA NA TRUE 1/8/2025 40.0269539332367 -105.274414453152
11 Muffin white NA Colorado Springs 7.25 NA TRUE 12/5/2024 38.8096790419363 -104.774434092202
12 NA NA NA NA NA NA NA NA NA NA
13 Muffin white NA Colorado Springs 7.25 NA TRUE 12/5/2024 38.8096790419363 -104.774434092202

Clean column names

Now we can use clean_names() to change column names to something that’s easy to process by code

DF_Cats = DF_Cats %>% clean_names()

#Notice how parenthesis and blank spaces are converted to _ . % are replaced with percent. # are replaced with number and all letters are now in lower case.
name color avg_weight_whole_number_s location age_round_quarter_percent na indoor date_surveyed lat lon
1 Luna orange 10 Denver 4.8 NA TRUE 01/14/2025 39.7511528988476 -104.997062810325
2 Willow black 9 Fort Collins 4.5 NA TRUE 1/13/2025 40.5498736980116 -105.088531978709
4 Miso gray 10 Boulder 6.3 NA TRUE 45636 40.0174811783286 -105.277762677534
5 Coco white 12 Colorado Springs NA NA TRUE 12/11/2024 38.8031143844515 -104.767835187917
6 Milo gray 10.76 Denver 3.75 NA TRUE Jan/10/2025 39.7420120757529 -104.954739780611
7 Bubbles brown NA Denver 10.25 NA TRUE 1/6/2025 39.7162284226902 -104.98374769985
8 Socks brown 8.1 Boulder 8.5 NA TRUE 1/05/2025 40.001310355816 -105.27534451548
9 Oreo black 11 Denver 6 NA TRUE 08/01/2025 39.748319373353 -104.975901295363
10 Fudge black 11.876 Boulder NA NA TRUE 1/8/2025 40.0269539332367 -105.274414453152
11 Muffin white NA Colorado Springs 7.25 NA TRUE 12/5/2024 38.8096790419363 -104.774434092202
12 NA NA NA NA NA NA NA NA NA NA
13 Muffin white NA Colorado Springs 7.25 NA TRUE 12/5/2024 38.8096790419363 -104.774434092202

Remove empty rows, columns, and constants

We use remove_empty() to get ride of empty rows and columns (default) and remove_constant() to get rid of columns with constant values.

DF_Cats = DF_Cats %>%
  remove_empty() %>% #removes both rows and columns that are empty
  remove_constant() #drops columns with a single constant value

#We had a column and a row with all NA values that were removed. Our "Indoor" column was also removed, given that all values were TRUE and it didn't add information.
name color avg_weight_whole_number_s location age_round_quarter_percent date_surveyed lat lon
1 Luna orange 10 Denver 4.8 01/14/2025 39.7511528988476 -104.997062810325
2 Willow black 9 Fort Collins 4.5 1/13/2025 40.5498736980116 -105.088531978709
4 Miso gray 10 Boulder 6.3 45636 40.0174811783286 -105.277762677534
5 Coco white 12 Colorado Springs NA 12/11/2024 38.8031143844515 -104.767835187917
6 Milo gray 10.76 Denver 3.75 Jan/10/2025 39.7420120757529 -104.954739780611
7 Bubbles brown NA Denver 10.25 1/6/2025 39.7162284226902 -104.98374769985
8 Socks brown 8.1 Boulder 8.5 1/05/2025 40.001310355816 -105.27534451548
9 Oreo black 11 Denver 6 08/01/2025 39.748319373353 -104.975901295363
10 Fudge black 11.876 Boulder NA 1/8/2025 40.0269539332367 -105.274414453152
11 Muffin white NA Colorado Springs 7.25 12/5/2024 38.8096790419363 -104.774434092202
13 Muffin white NA Colorado Springs 7.25 12/5/2024 38.8096790419363 -104.774434092202

Examining duplicate entries

With get_dupes() you specify the variable combination to search for duplicates and it returns the duplicated rows along with a count of the number of rows sharing that combination of duplicated values.

DF_Cats %>% get_dupes() #examining duplicates
##     name color avg_weight_whole_number_s         location
## 1 Muffin white                      <NA> Colorado Springs
## 2 Muffin white                      <NA> Colorado Springs
##   age_round_quarter_percent date_surveyed              lat               lon
## 1                      7.25     12/5/2024 38.8096790419363 -104.774434092202
## 2                      7.25     12/5/2024 38.8096790419363 -104.774434092202
##   dupe_count
## 1          2
## 2          2
DF_Cats = DF_Cats[-11,] #removing duplicate row
#In this case we examine the last row with "Muffin" as the name and decide it's a duplicate entry due perhaps to input error. It is therefore removed.
name color avg_weight_whole_number_s location age_round_quarter_percent date_surveyed lat lon
1 Luna orange 10 Denver 4.8 01/14/2025 39.7511528988476 -104.997062810325
2 Willow black 9 Fort Collins 4.5 1/13/2025 40.5498736980116 -105.088531978709
4 Miso gray 10 Boulder 6.3 45636 40.0174811783286 -105.277762677534
5 Coco white 12 Colorado Springs NA 12/11/2024 38.8031143844515 -104.767835187917
6 Milo gray 10.76 Denver 3.75 Jan/10/2025 39.7420120757529 -104.954739780611
7 Bubbles brown NA Denver 10.25 1/6/2025 39.7162284226902 -104.98374769985
8 Socks brown 8.1 Boulder 8.5 1/05/2025 40.001310355816 -105.27534451548
9 Oreo black 11 Denver 6 08/01/2025 39.748319373353 -104.975901295363
10 Fudge black 11.876 Boulder NA 1/8/2025 40.0269539332367 -105.274414453152
11 Muffin white NA Colorado Springs 7.25 12/5/2024 38.8096790419363 -104.774434092202

Homogenizing dates

Dates can be tricky, coming in many different formats. In our date_surveyed column we notice dates in several formats. Some months are represented with a single digit, others with two, and others with letters (01 vs. 1 vs. Jan). When importing from excel we might also receive dates in a numeric encoding system such as 45636. And finally, one of our entries is in what seems to be day/month/year format (08/01/2025) instead of month/day/year. This format is common practice in other countries and is good to check for when working with multiple data sets or when evaluating entry errors.

  1. First we’ll change the numeric value (45636) to a Date format using convert_to_date() from the janitor package. convert_to_date() recognizes Excel date and numeric formats of character or text classes and converts them to a Date class.

  2. Second we’ll use the mdy() and dmy() functions from the lubridate package to change month/day/year and day/month/year entries to a Date class.

# library(lubridate)

DF_Cats$date_surveyed_ = as.Date("")
#Create a new column in Date format to populate with our transformed Dates

DF_Cats$date_surveyed_[3] = convert_to_date(DF_Cats$date_surveyed[3])
#converting our numeric entry (45636) to a date class

DF_Cats$date_surveyed_[8] = dmy(DF_Cats$date_surveyed[8])
#converting our day/month/year entry to a date class

DF_Cats$date_surveyed_[-c(3,8)] = mdy(DF_Cats$date_surveyed[-c(3,8)])
#converting the rest of the month/day/year entries to Date format

DF_Cats = DF_Cats[,-6]
#Removing our original raw dates column
name color avg_weight_whole_number_s location age_round_quarter_percent lat lon date_surveyed_
1 Luna orange 10 Denver 4.8 39.7511528988476 -104.997062810325 2025-01-14
2 Willow black 9 Fort Collins 4.5 40.5498736980116 -105.088531978709 2025-01-13
4 Miso gray 10 Boulder 6.3 40.0174811783286 -105.277762677534 2024-12-10
5 Coco white 12 Colorado Springs NA 38.8031143844515 -104.767835187917 2024-12-11
6 Milo gray 10.76 Denver 3.75 39.7420120757529 -104.954739780611 2025-01-10
7 Bubbles brown NA Denver 10.25 39.7162284226902 -104.98374769985 2025-01-06
8 Socks brown 8.1 Boulder 8.5 40.001310355816 -105.27534451548 2025-01-05
9 Oreo black 11 Denver 6 39.748319373353 -104.975901295363 2025-01-08
10 Fudge black 11.876 Boulder NA 40.0269539332367 -105.274414453152 2025-01-08
11 Muffin white NA Colorado Springs 7.25 38.8096790419363 -104.774434092202 2024-12-05

Rounding values

In our avg_weight_whole_number_s column we notice that our values are supposed to be only whole numbers, but not all of them are. round_half_up() from janitor will round all our values up to the nearest whole number. Compare this to round(), in which R by default uses “banker’s rounding” to round halves to the nearest even number.

Our other column age_round_quarter_percent takes age data, but only values of quarters. round_to_fraction() will round the values to the nearest specified denominator.

#Rounding comparison example below

#values from .5 to 4.5, increasing by 1, are rounded by R default
round(seq(0.5, 4.5, 1))
## [1] 0 2 2 4 4
#values rounded by round_half_up() from janitor
round_half_up(seq(0.5, 4.5, 1))
## [1] 1 2 3 4 5
DF_Cats$avg_weight_whole_number_s = round_half_up(as.numeric(DF_Cats$avg_weight_whole_number_s))
#as.numeric() is needed because our column "avg_weight_whole_number_s" is currently of class character, given that it has NA values. 

DF_Cats$age_round_quarter_percent = round_to_fraction(as.numeric(DF_Cats$age_round_quarter_percent), denominator = 4)
#rounding all age values to quarters
name color avg_weight_whole_number_s location age_round_quarter_percent lat lon date_surveyed_
1 Luna orange 10 Denver 4.75 39.7511528988476 -104.997062810325 2025-01-14
2 Willow black 9 Fort Collins 4.50 40.5498736980116 -105.088531978709 2025-01-13
4 Miso gray 10 Boulder 6.25 40.0174811783286 -105.277762677534 2024-12-10
5 Coco white 12 Colorado Springs NA 38.8031143844515 -104.767835187917 2024-12-11
6 Milo gray 11 Denver 3.75 39.7420120757529 -104.954739780611 2025-01-10
7 Bubbles brown NA Denver 10.25 39.7162284226902 -104.98374769985 2025-01-06
8 Socks brown 8 Boulder 8.50 40.001310355816 -105.27534451548 2025-01-05
9 Oreo black 11 Denver 6.00 39.748319373353 -104.975901295363 2025-01-08
10 Fudge black 12 Boulder NA 40.0269539332367 -105.274414453152 2025-01-08
11 Muffin white NA Colorado Springs 7.25 38.8096790419363 -104.774434092202 2024-12-05

Filling in missing values

Is this something that we want to do? Or should lines with missing values be removed? In this example we’ll fill in the missing values, but will provide code for how to delete those entries, if that were preferred. We’ll be working with the columns age_round_quarter_percent and avg_weight_whole_number_s.

Let’s say we determine that both columns are more or less normally distributed through the shapiro test. And so we decide to fill in NA values with the mean.

shapiro.test(DF_Cats$age_round_quarter_percent) #p values are > .05
## 
##  Shapiro-Wilk normality test
## 
## data:  DF_Cats$age_round_quarter_percent
## W = 0.95313, p-value = 0.7427
shapiro.test(DF_Cats$avg_weight_whole_number_s)
## 
##  Shapiro-Wilk normality test
## 
## data:  DF_Cats$avg_weight_whole_number_s
## W = 0.93444, p-value = 0.5573
#Replacing NA values in "age_round_quarter_percent" with the mean of the column, using round_to_fraction() to round mean to the nearest quarter.
DF_Cats$age_round_quarter_percent = ifelse(is.na(DF_Cats$age_round_quarter_percent), 
                      round_to_fraction(mean(DF_Cats$age_round_quarter_percent,
                             na.rm = TRUE), denominator = 4),
                      DF_Cats$age_round_quarter_percent)

#Replacing NA values in "avg_weight_whole_number_s" with the mean of the column, using round_half_up() to get rounded up whole numbers.
DF_Cats$avg_weight_whole_number_s = ifelse(is.na(DF_Cats$avg_weight_whole_number_s), 
                      round_half_up(mean(DF_Cats$avg_weight_whole_number_s,
                             na.rm = TRUE)),
                      DF_Cats$avg_weight_whole_number_s)
name color avg_weight_whole_number_s location age_round_quarter_percent lat lon date_surveyed_
1 Luna orange 10 Denver 4.75 39.7511528988476 -104.997062810325 2025-01-14
2 Willow black 9 Fort Collins 4.50 40.5498736980116 -105.088531978709 2025-01-13
4 Miso gray 10 Boulder 6.25 40.0174811783286 -105.277762677534 2024-12-10
5 Coco white 12 Colorado Springs 6.50 38.8031143844515 -104.767835187917 2024-12-11
6 Milo gray 11 Denver 3.75 39.7420120757529 -104.954739780611 2025-01-10
7 Bubbles brown 10 Denver 10.25 39.7162284226902 -104.98374769985 2025-01-06
8 Socks brown 8 Boulder 8.50 40.001310355816 -105.27534451548 2025-01-05
9 Oreo black 11 Denver 6.00 39.748319373353 -104.975901295363 2025-01-08
10 Fudge black 12 Boulder 6.50 40.0269539332367 -105.274414453152 2025-01-08
11 Muffin white 10 Colorado Springs 7.25 38.8096790419363 -104.774434092202 2024-12-05

If you want to remove the NA values, drop_na() from the tidyverse package is a simple option to remove all rows with NA values in the specified columns.

#library(tidyr)

#if no columns are specified, rows with NA values in any column will be removed.
DF_Cats %>% drop_na()
##       name  color avg_weight_whole_number_s         location
## 1     Luna orange                        10           Denver
## 2   Willow  black                         9     Fort Collins
## 3     Miso   gray                        10          Boulder
## 4     Coco  white                        12 Colorado Springs
## 5     Milo   gray                        11           Denver
## 6  Bubbles  brown                        10           Denver
## 7    Socks  brown                         8          Boulder
## 8     Oreo  black                        11           Denver
## 9    Fudge  black                        12          Boulder
## 10  Muffin  white                        10 Colorado Springs
##    age_round_quarter_percent              lat               lon date_surveyed_
## 1                       4.75 39.7511528988476 -104.997062810325     2025-01-14
## 2                       4.50 40.5498736980116 -105.088531978709     2025-01-13
## 3                       6.25 40.0174811783286 -105.277762677534     2024-12-10
## 4                       6.50 38.8031143844515 -104.767835187917     2024-12-11
## 5                       3.75 39.7420120757529 -104.954739780611     2025-01-10
## 6                      10.25 39.7162284226902  -104.98374769985     2025-01-06
## 7                       8.50  40.001310355816  -105.27534451548     2025-01-05
## 8                       6.00  39.748319373353 -104.975901295363     2025-01-08
## 9                       6.50 40.0269539332367 -105.274414453152     2025-01-08
## 10                      7.25 38.8096790419363 -104.774434092202     2024-12-05

Some Analysis and Descriptive Statistics

Using get_dupes() from Janitor to find similar data entries

Lets say you wanted to see if any cats were surveyed in the same city and share the same hair color. This might give insights into the data, potentially suggesting that these cats are related. With get_dupes() you can specify the columns you want to compare, and then get an output of the rows that share those characteristics along with a count of those rows.

#Are Coco and Muffin related? Do they live near each other?
DF_Cats %>% get_dupes(color,location)
##   color         location dupe_count   name avg_weight_whole_number_s
## 1 white Colorado Springs          2   Coco                        12
## 2 white Colorado Springs          2 Muffin                        10
##   age_round_quarter_percent              lat               lon date_surveyed_
## 1                      6.50 38.8031143844515 -104.767835187917     2024-12-11
## 2                      7.25 38.8096790419363 -104.774434092202     2024-12-05

Using tabyl() from Janitor to understand column categories

With tabyl() we can easily see how many cats of the different colors are present in each city. tabyl() can compare up to three columns and results can be styled through **adorn_*()** functions.

DF_Cats %>% tabyl(color,location)
##   color Boulder Colorado Springs Denver Fort Collins
##   black       1                0      1            1
##   brown       1                0      1            0
##    gray       1                0      1            0
##  orange       0                0      1            0
##   white       0                2      0            0
#Compare the above result with the adorned version below
DF_Cats %>% tabyl(color,location) %>% 
  adorn_totals(c("row","col")) %>% #add totals to rows and columns
  adorn_percentages() %>% #calculate percentages
  adorn_pct_formatting(rounding = "half up", digits = 0) %>% #specify number of percent decimals and adds the % symbol
  adorn_ns() %>% #include the total number of entries in parenthesis
  adorn_title("combined") #Add a title for rows, columns or both combined
##  color/location Boulder Colorado Springs   Denver Fort Collins     Total
##           black 33% (1)           0% (0)  33% (1)      33% (1) 100%  (3)
##           brown 50% (1)           0% (0)  50% (1)       0% (0) 100%  (2)
##            gray 50% (1)           0% (0)  50% (1)       0% (0) 100%  (2)
##          orange  0% (0)           0% (0) 100% (1)       0% (0) 100%  (1)
##           white  0% (0)         100% (2)   0% (0)       0% (0) 100%  (2)
##           Total 30% (3)          20% (2)  40% (4)      10% (1) 100% (10)

Using summarise() from the dplyr package to get summary statistics

summarise() is good to get specified summary statistics from a grouped variable. In the example below we’ll calculate weight mean, median, standard deviation, and variance, grouping by location.

DF_Cats %>%
  group_by(location) %>%
  summarise(n = n(), # Count by location
            mean_weight = mean(avg_weight_whole_number_s, na.rm = TRUE), # Mean
            median_weight = median(avg_weight_whole_number_s, na.rm = TRUE), # Median
            sd_weight = sd(avg_weight_whole_number_s, na.rm = TRUE), # Standard deviation
            var_weight = var(avg_weight_whole_number_s, na.rm = TRUE)) # Variance
## # A tibble: 4 × 6
##   location             n mean_weight median_weight sd_weight var_weight
##   <chr>            <int>       <dbl>         <dbl>     <dbl>      <dbl>
## 1 Boulder              3        10            10       2          4    
## 2 Colorado Springs     2        11            11       1.41       2    
## 3 Denver               4        10.5          10.5     0.577      0.333
## 4 Fort Collins         1         9             9      NA         NA

Using skim() from the skimr package to conveniently summarize the entire data set or by grouped variables

The output using skim() is slightly different from summarise(), but it’s perhaps a little quicker and uses less code. If no parameters are passed, skim() will summarize the entire data set by variable class.

# library(skimr)
DF_Cats %>% skim()
Data summary
Name Piped data
Number of rows 10
Number of columns 8
_______________________
Column type frequency:
character 5
Date 1
numeric 2
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
name 0 1 4 7 0 10 0
color 0 1 4 6 0 5 0
location 0 1 6 16 0 4 0
lat 0 1 15 16 0 10 0
lon 0 1 16 17 0 10 0

Variable type: Date

skim_variable n_missing complete_rate min max median n_unique
date_surveyed_ 0 1 2024-12-05 2025-01-14 2025-01-07 9

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
avg_weight_whole_number_s 0 1 10.30 1.25 8.00 10.00 10.00 11.00 12.00 ▂▂▇▃▃
age_round_quarter_percent 0 1 6.42 1.93 3.75 5.06 6.38 7.06 10.25 ▇▅▇▂▂ 
#Below we use skim() to get summary statistics of age, and weight by color
DF_Cats %>%
  group_by(color) %>%
  select(avg_weight_whole_number_s,age_round_quarter_percent) %>%
  skim()
Data summary
Name Piped data
Number of rows 10
Number of columns 3
_______________________
Column type frequency:
numeric 2
________________________
Group variables color

Variable type: numeric

skim_variable color n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
avg_weight_whole_number_s black 0 1 10.67 1.53 9.00 10.00 11.00 11.50 12.00 ▇▁▁▇▇
avg_weight_whole_number_s brown 0 1 9.00 1.41 8.00 8.50 9.00 9.50 10.00 ▇▁▁▁▇
avg_weight_whole_number_s gray 0 1 10.50 0.71 10.00 10.25 10.50 10.75 11.00 ▇▁▁▁▇
avg_weight_whole_number_s orange 0 1 10.00 NA 10.00 10.00 10.00 10.00 10.00 ▁▁▇▁▁
avg_weight_whole_number_s white 0 1 11.00 1.41 10.00 10.50 11.00 11.50 12.00 ▇▁▁▁▇
age_round_quarter_percent black 0 1 5.67 1.04 4.50 5.25 6.00 6.25 6.50 ▇▁▁▇▇
age_round_quarter_percent brown 0 1 9.38 1.24 8.50 8.94 9.38 9.81 10.25 ▇▁▁▁▇
age_round_quarter_percent gray 0 1 5.00 1.77 3.75 4.38 5.00 5.62 6.25 ▇▁▁▁▇
age_round_quarter_percent orange 0 1 4.75 NA 4.75 4.75 4.75 4.75 4.75 ▁▁▇▁▁
age_round_quarter_percent white 0 1 6.88 0.53 6.50 6.69 6.88 7.06 7.25 ▇▁▁▁▇

Visualizations using ggplot2

I like using ggplot2 over base R or other graphics because of how flexible, consistent, and visually appealing it is. ggplot2 follows a consistent underlying grammar of graphics that’s easy to follow. With some added packages we’ll see below, you can customize visuals even further and make them interactive.

Box and Scatter plots using ggplot2

First we create a box plot of average weight by location and assign the plot the name of “Box_Plot”

# library(ggplot2)
Box_Plot = ggplot(data = DF_Cats, 
       mapping = aes(x = location, y = avg_weight_whole_number_s)) + 
  labs(x = "Location", 
       y = "Average Weight") + 
  geom_boxplot() +
  theme_dark()

Box_Plot

Second we create a scatter plot of average weight by location and color, assigning it the name of “Scatter_Plot”

Scatter_Plot = ggplot(data = DF_Cats, 
       mapping = aes(age_round_quarter_percent, avg_weight_whole_number_s, colour = location)) + 
  geom_point(size = 3) +
  geom_smooth(method = lm, color = "red", se = TRUE) +
  labs(colour = "City",
       x = "Age (Years)",
       y = "Average Weight (Pounds)") +
  theme_classic()

Scatter_Plot

Using ggplot2 extensions plotly, patchwork, and ggforce to enhance graphs

plotly has a lot of great features that make your graphics interactive. Below we’ll make our box plot interactive.

Note: that in a non HTML format, the plot will be static. To view the interactive html version download this file and open it on a browser.

# library(plotly)
ggplotly(Box_Plot)

ggforce enhances scatter plots by allowing you to highlight certain points, drawing an ellipse around points that adhere to a given filter. A sign pointing to the ellipse can also be added. Below we will add the ellipse and sign to our saved “Scatter_Plot” created above.

# library(ggforce)
Scatter_Plot = Scatter_Plot +
  geom_mark_ellipse(
    data = DF_Cats %>% filter(avg_weight_whole_number_s > 10),
    aes(x0 = 7.5, y0= 9, label = "These are cats heavier than 10 pounds", group = -1L),
    fill = "red", colour = "black", alpha = .2, label.fontsize = 9)

Scatter_Plot

patchwork is great for letting you combined multiple plots into one and be able to visualize or save them as one image.

# library(patchwork)
Box_Plot / Scatter_Plot

Mapping data using mapview

If you have latitude and longitude coordinates in your data, you can map each entry as a point on a map and add whichever label you want.

Note: that in a non HTML format, the map will be static. To view the interactive html version download this file and open it on a browser.

# library(sf)
# library(mapview)
DF_Cats_Map <- st_as_sf(DF_Cats, coords = c("lon", "lat"),  crs = 4326)
#spatial data must first be converted to simple features object in order for it to be read and mapped appropriately
mapview(DF_Cats_Map, map.types ="CartoDB.Positron",label = DF_Cats_Map$name)